This is a preview. Log in through your library . Abstract We consider a discrete time hidden Markov model where the signal is a stationary Markov chain. When conditioned on the observations, the ...

CATALOG DESCRIPTION: Fundamentals of random variables; mean-squared estimation; limit theorems and convergence; definition of random processes; autocorrelation and stationarity; Gaussian and Poisson ...

CATALOG DESCRIPTION: Advanced topics in random processes: point processes, Wiener processes; Markov processes, spectral representation, series expansion of random processes, linear filtering, Wiener ...

The conditional probability integral transform, used in goodness-of-fit tests, is compared with its Bayesian counterpart, the predictive probability integral transform, and it is shown that under ...

Stochastic differential equations (SDEs) and random processes form a central framework for modelling systems influenced by inherent uncertainties. These mathematical constructs are used to rigorously ...

Studies axioms, counting formulas, conditional probability, independence, random variables, continuous and discrete distribution, expectation, joint distributions, moment generating functions, law of ...